Big data analytics in the pharmaceutical industry is changing drug development and delivery. Enormous volumes of data enable a pharmaceutical company to make more informed decisions and work much more efficiently.

Predictive analytics facilitates demand estimation and proper supply chain management, hence ensuring drugs are always available. Personal medicine ensures that treatment is given according to the individual patient's condition and is therefore effective. Real-time analytics in clinical trials run the changes with patients, and on time, spot any emerging problems, hence fast-tracking drug development. Big data also plays a very key role in discovering side effects and improving the safety of drugs.

In the future, as data analytics further develops, it could be that, by the big strides ahead in the treatment of pharmaceuticals, there are more effective treatments and better health outcomes for all.

Gmail has become a huge platform with almost 1.2 billion monthly users and holds a 20% share of the global email market. However, Gmail is not really secure as they require personal information such as name, interests, who is one talking to, and many more things. Hence, for trusting Gmail, several tips should be implemented for ensuring maximum security. • Big Data plays a big role in cybersecurity and malware protection. Machine Learning tools are capable of understanding newest thus it is advisable to invest in email specific antivirus software. • Enabling two-step verification, again made reliable by the Big Data. Along with this is the easy-to-use update which adds to the security. • Upgrading the browser whenever possible reduces changes of cybercrimes. • Using a sophisticated password. Read more at: https://www.smartdatacollective.com/4-brilliant-ways-to-use-big-data-to-boost-gmail-security/

A large number of tables with thousands of rows and columns. Data keeps flowing in from multiple live sources and is rapidly changing . This is what characterizes Big Data. Datasets are becoming vast and hence more complex for analysis. Without the correct tools,it would be difficult to use this data to draw insights. In this regard,three trends have come to the forefront- IoT,querying techniques and cloud computing.The advent of IoT has digitised everything. There are sensors and wires that chanel data which is then manipulated and analyzed to get results.Querying data i.e. extracting information from data is the first step before analysis and Cloud has come to play a major role in data storage. The three trends connected have paved a path for growth in technology.

Read more at: https://www.sisense.com/blog/waking-up-the-world-of-big-data/

It’s really fascinating to see how we are generating an incredible amount of data every minute. Plenty of blogs and articles have been written stating that the better utilization of this data may result in more profit generation for the organization. So here are some tips to begin with the path of better utilization.

Currently what most of the organizations see is they are Creating data, and they want to generate some additional revenue from it, but they miss how to link these two aspects. 

To begin with, they must consider to use the Big Data Engines like Hadoop, Google Bigdata, Horton Works, MongoDB or anything similar. They help to find the patterns and correlations between different types of data.

Next is Data Warehousing. Data warehousing is not just maintaining a database. It has many advantages over the traditional databases. 

And another important step is Data Visualization. From the above two steps we have the data properly managed and organized. In this step we visualize the data in a productive manner.

Read More at https://www.informationweek.com/big-data/real-world-tools-to-help-navigate-a-data-driven-world/a/d-id/1332377?

For any organization, it is necessary to keep a track of the data created, data pulled, the source from where it is pulled, the format of the data they have been using till now, who can access this data and many more. There are many strategies for managing the data but there’s nothing as a perfect strategy. It varies from organization to organization and types of data. Another important factor is size of organization and its budget. The following are the three factors that can help in managing the data in a better way i.e. as per the GDPR rules.

1. Centralization of data

2. Automating data management 

3. Measuring Success.

Read More at https://www.analyticsinsight.net/how-to-overcome-the-challenges-in-managing-big-data

As per the reports by SNS Telecom & IT, Big Data investment will cross $3.3 Billion by the end of 2018. Real time and historic data is rapidly gaining attraction towards diverse range and various sectors. There are many key findings in the report generated by SNS which ensures the widening scope of Big Data. 

For detailed information, visit:

https://www.analyticsinsight.net/big-data-3-3-billion-opportunity-automotive-industry-says-sns-telecom/

After Artificial Intelligence and Machine Learning, the next most emerging field in todays world is the field of Data Science. It is said to be the cousins of AI and ML and mainly deals with data. It intakes data, uses processes, algorithms and scientific methods to extract knowledge and valuable data from large data sets. This field is need of each and every type of organization. Whether it be business or an IT firm, every organization needs data for improvement. Thus, outcomes from the processing of data are further used for decision making and for improving current functioning.

People often gets confused between Data Science, Data Analytics and Big Data. The key difference between them is that Data Analytics and Big Data are components of Data Science. Data Science extract values from the output of Data Analytics and Big Data to solve problems.
The goal of Data Science is to extract business-focused insights from business. This could help organizations in many ways.

Read more about this topic at: https://www.cio.com/article/3285108/data-science/what-is-data-science-a-method-for-turning-data-into-value.html

Big Data and Analytics are not just confined to online services anymore, they have made their way to changing things offline as well. One of the fields where they have a significant impact is manufacturing. Big Data has been able to perform Predictive Maintenance. It can help in analyzing performance of individual machines. Not just this, they can even help in making improved strategic decisions for better outcomes. Big data analytics are changing manufacturing for the better. The only question is, will you be coming along for the ride? To know more: 

https://www.smartdatacollective.com/how-big-data-and-analytics-are-changing-manufacturing-for-the-better/

Big data systems have been growing more efficient every day. The data trends have been ground breaking in 2018 as well. There has been implementation of one of the most enhanced cybersecurity model ever. Not just cyber crime, but the crime rate in general has started to come down all around the world in the last 20 years. The expansion of Internet of Things is another amazing influence of Big Data in todays industry. Artificial Intelligence has actually started reaching out to the masses. A number of new occupations have started coming up already. There are plenty of other news from around the world on Big Data. Find them out at: 

https://www.smartdatacollective.com/buzzworthy-big-data-trends-year/

Big data has started to play a vital role in gaming industries nowadays. Providing individual experience and maximizing the advertising of its products, gaming industry is far beyond ready to take big data to its peak. They have implemented techniques to collect data directly from online sources. They have data for marketing like never before. Big data has a new definition, a new meaning to everyone in the gaming industry, big data means a better consumer experience. Read more at: 

https://www.smartdatacollective.com/online-gaming-industry-uses-big-data-analytics-grow/

Hadoop has helped in shaping the future of weather forecasting in a much better way than the analyst could have ever predicted.  More factors can now be evaluated in the models. Also, the advancement in big data has allowed meteorologists to highly depend on the digital models. How exactly will Hadoop affect the future of meteorology? Find out at:

https://www.smartdatacollective.com/hadoop-predictive-analytics-improves-weather-forecasting-models/

A number of trends lately has helped in shaping the Big Data. But, digital transformation is one of the key ones of all. It is being predicted that it might change the ways of data science profession in the years to come. Key features like flexibility, access to expertise, data security, etc. has helped it overcome most of the challenges. Many are saying that data transformation is the next major phase of Big Data. Find out more at: 

https://www.smartdatacollective.com/digital-transformation-unexpected-renaissance-age-of-big-data/

Cloud computing has helped in evolving the robot technology to greater heights. More information and data are being delivered in real time. Robots have started to learn much quicker. Robot movies have started to become reality. They have started helping us with healthcare. Read more at: 

https://www.smartdatacollective.com/merging-big-data-cloud-computing-future-robotics/

A number of times, users tend to fill in false demographic inputs in different surveys to earn rewards and enjoy premium benefits. But, visual data may change that for these organisations. Facial Recognition can help authenticate those inputs and reduce the rate of response errors in the survey, making it way more valid than before. Read more:

https://www.smartdatacollective.com/big-data-facial-recognition-tools-launch-consumer-research/

Every business is undergoing a digital transition, which in turn keeps adding huge amount of information every day. Big Data is one of the best tool in today’s world when we talk about problem solving. It has truly helped us in overcoming the age-old challenges, and now it is doing the same with different businesses. Read more at:

https://www.smartdatacollective.com/big-data-unlocks-key-age-old-challenges-businesses-face/

Most of the organisations right now are collecting a lot of data every day and doing all sorts of manipulations with it. And now, since we have the right tools, it is easier for us to make better decisions and have more cost efficiency. But, the machine all on its own can never be of the best use for data management. A co-operation between humans and machines is becoming essential at all levels. Read more at: 

https://www.computer.org/web/computingnow/insights/content?g=53319&type=article&urlTitle=5-reasons-the-future-of-big-data-requires-human-machine-cooperation

According to authors, the implication of big data is the quantity is paramount, the returns generated do not match the quantity of data generated. Experts point out, it is not per se the data that should be big, but the primary factor that counts is the diversity of data, the amount of richness they provide and the focus on accelerating human understanding of data , which has the potential to create output subject to increasing returns. More data retards innovation, the speed of experimentation and iteration. However IT teams helps in bringing order to chaos, in data and analytics, by managing data infrastructure, such as data warehouses and production processes . Data scientists, who’re occupying the space between IT and business consumers , have made enormous strides in getting grip on their data, analyzing and acting on it, thereby avoiding imbalance. Read more at https://aitrends.com/big-data/three-big-data-developments-no-one-is-talking-about/

Though there has been a lot of excitement around Big Data and the potential it holds for making businesses efficient, the significant risks surrounding it in the longer run shouldn’t be overlooked. Some of the most critical issues relating to Big Data are determining what kind of information can be shared and with whom, verifying transmission of cyber information without the threat of leakage and accurately analyzing the available data through the right algorithms and models. The two serious limitations to analysis of huge data sets are overestimation of the predictive capabilities of analytics and misrepresentation and manipulation of data to produce favorable results. Over-dependence on data as a base for decision making can be a risky proposition, especially for consumer-driven business and research organisations. Read more at: http://www.financialexpress.com/industry/technology/big-data-paradox-how-a-business-can-become-a-target-for-cyber-criminals/751246/

In spite of a lot of buzz about big data and data analytics, only few organizations like Amazon and Google have managed to exploit them to the potential. With the shortage of doctors, lack of infrastructure and only 5% Indian population covered by health insurance, healthcare system in India is in a delicate condition. If used in the right way, the data analytics in healthcare system can push the sector to excellence by retaining right customers and assisting in providing cost efficient operations. Data analytics can also assist the insurers to increase the coverage of healthcare insurance from 5% to 50% of the Indian population. Read more at http://health.economictimes.indiatimes.com/news/industry/healthcare-analytics-in-india-opportunities-and-challenges/59028069

Fast data is the application of big data analytics to smaller data sets in real-time in order to solve a problem or create business value. The goal of fast data analytics is to quickly gather and mine structured and unstructured data so that customer experience can be improved by creating a more streamlined process for marketing strategies and customer service implementation. It has been observed that fast data analytics helped businesses turn their raw machine data into actionable insights by tracking transactions, identifying issues with hardware and software, and reducing customer complaints. It also helped in staying compliant with government regulations, avoiding preventable losses and improving the personnel’s efficiency by pinpointing errors. Thus, fast data analytics services significantly improve business’ customer experience by solving issues faster and more efficiently. Read more at: http://www.datasciencecentral.com/profiles/blogs/how-you-can-improve-customer-experience-with-fast-data-analytics?xg_source=activity